AI Briefing#llm
The latest AI and developer news about #llm, with the original source and a short summary.
The latest AI and developer news about #llm, with the original source and a short summary.
Introduces the latest research trends in generative models (SGM) and LLMs presented at NeurIPS 2022.
This introduces PromptRank, a technique that uses LLMs to find efficient document paths for complex Multi-hop Question Answering (MQA).
PromptRank is a technique that combines unsupervised retrieval with an LLM reranker to improve data efficiency in multi-hop question answering.
LG AI Research explored the latest vision research trends at CVPR 2023 and discussed the limitations of image captioning through the NICE workshop.
LG AI Research analyzed the computer vision research trends presented at CVPR 2023 and visual reasoning techniques leveraging LLMs.
LG AI Research proposed the QASA benchmark and a multi-step reasoning methodology for deep reasoning on scientific literature.
LG AI Research has released the QASA methodology and benchmark to address complex reasoning in scientific papers.
We look at the types of training data and the multifaceted evaluation metrics that determine the performance of LLMs, the core of generative AI.
This provides a detailed analysis of data training, evaluation, and model architecture for Large Language Models (LLMs), the core of generative AI.
LG AI Research's EXAONE model ranked first on KorQuAD 2.0, a benchmark for evaluating Korean machine reading comprehension performance.
Introduces five major AI research trends and key papers in CV, LLM, and more that drew attention at NeurIPS 2023.
This introduces the latest research trends and key papers on RLHF and RLAIF presented at NeurIPS 2023.
This summarizes the latest research trends presented at NeurIPS 2023, covering the performance, safety, efficiency, and tool-use capabilities of LLMs.
Introduces the latest research trends in RLHF and RLAIF for improving LLM performance, along with the DPO and Fine-Grained RLHF methodologies.
This looks at the latest research trends in LLMs presented at NeurIPS 2023, including performance evaluation, safety, tool use, and efficient fine-tuning.
Introduces recent research that improves OOD detection performance by generating synthetic outliers in the text and image domains.
Introduces research that improves visual OOD detection performance by generating text-based outliers using LLMs.
This piece examines the concept of foundation models for handling time series data and their classification based on how they utilize LLMs.
This introduces the latest research methodologies of Foundation models for tackling time series data, classified according to how they utilize LLMs.
LG AI Research and KAIST took first place in the EHRSQL task at NAACL 2024 with a highly reliable Text-to-SQL model for extracting medical data.
LG AI Research, together with KAIST, took 1st place at the NAACL 2024 workshop with a reliable Text-to-SQL model that improves access to medical data.
LG AI Research presented research on the model compression technique LeSS and LLM structured pruning at an ICLR 2024 workshop.
A joint study by LG AI Research and the University of Michigan won the NAACL 2024 Social Impact Award for analyzing LLMs' understanding of cultural common sense and their biases.
A joint study by LG AI Research and the University of Michigan analyzing LLMs' understanding of cultural common sense won the NAACL 2024 Social Impact Award.